SPADE: Sparsity-Adaptive Equalization for MMwave Massive MU-MIMO

Abstract

We propose SParsity-ADaptive Equalization (SPADE), a novel approach to reduce the effective number of multiplications in sparse inner products by adaptively skipping multiplications that have little to no effect on the result. We apply SPADE to beamspace linear minimum mean square error (LMMSE) spatial equalization in all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) systems. We propose a SPADE-based architecture that mutes insignificant multiplications to offer power savings. We use simulation results with line-of-sight (LoS) and non-LoS mmWave channel models to demonstrate that SPADE-LMMSE performs on par with state-of-the-art beamspace equalizers in terms of bit error-rate, while requiring significantly lower preprocessing complexity.